Abstract
Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.
Highlights
Widespread deployments of robotics, assembly and production, automation, machine intelligence, and virtual reality applications requires high performance computing resources available close to the point-of-service [1]
We demonstrate how the reinforcement learning assisted mobile edge server placement can be performed with multi-agent reinforcement learning coordination techniques
We have shown a multi-agent reinforcement learning based solution for the placement of edge services in a mobile network, such that the network latency is minimized and load on edge servers is balanced
Summary
Widespread deployments of robotics, assembly and production, automation, machine intelligence, and virtual reality applications requires high performance computing resources available close to the point-of-service [1]. Integration of smart services, such as predictive analysis, and delay-intolerant applications, such as healthcare applications, in current cellular architecture with limited battery lifetimes and processing power of edge (mobile and IoT) devices have called for the re-imagination of cloud computing architecture. The traditional cloud-centric architecture provides flexibility and significant computation power. To circumvent delay in the traditional cloud-centric architecture, several network architectures have been proposed with the idea of bringing the cloud nearer to user devices [2]. One such architecture is edge computing that provides a virtualized application layer between edge devices and cloud engine in an existing network infrastructure. Edge computing introduces distributed control systems replacing the single remote centralized control-centre or cloud allowing the processing of data near the edge of the network with enhanced scalability
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